In this repository, we provide software and demonstrations related to the following paper:
- Robust variance-regularized risk minimization with concomitant scaling. Matthew J. Holland. AISTATS 2024.
This repository contains code which can be used to faithfully reproduce all the experimental results given in the above paper, and it can be easily applied to more general machine learning tasks outside the examples considered here.
A table of contents for this README file:
- Setup: initial software preparation
- Setup: preparing the benchmark data sets
- Getting started
- Demos and visualization
- Safe hash value
To begin, please ensure you have the prerequisite software used in the setup of our mml repository.
Next, make a local copy of the repository and create a virtual environment for working in as follows:
$ git clone https://github.com/feedbackward/mml.git
$ git clone https://github.com/feedbackward/bdd-mv.git
$ conda create -n bdd-mv python=3.9 jupyter matplotlib pip pytables scikit-learn scipy unzip
$ conda activate bdd-mv
Having made (and activated) this new environment, we would like to use pip to install the supporting libraries for convenient access. This is done easily, by simply running
(bdd-mv) $ cd [mml path]/mml
(bdd-mv) $ pip install -e ./
with the [mml path] placeholder replaced with the path to wherever you placed the repositories. If you desire a safe, tested version of mml, just run
(bdd-mv) $ git checkout [safe hash mml]
and then do the pip install -e ./ command mentioned above. The [safe hash mml] placeholder is to be replaced using the safe hash value given at the end of this document.
Please follow the instructions under "Acquiring benchmark datasets" using our mml repository. The rest of this README assumes that the user has prepared any desired benchmark datasets, stored in a local data storage directory (default path is [path to mml]/mml/mml/data as specified by the variable dir_data_towrite in mml/mml/config.py.
One important step is to ensure that once you've acquired the benchmark data using mml, you must ensure that bdd knows where that data is. To do this, set dir_data_toread in setup_data.py to the directory housing the HDF5 format data sub-directories (default setting: your home directory).
We have basically three types of files:
-
Setup files: these take the form
setup_*.py.- Configuration for all elements of the learning process, with one setup file for each of the following major categories: learning algorithms, data preparation, learned model evaluation, loss functions, models, result processing, and general-purpose training functions.
-
Driver scripts: just one at present, called
learn_driver.py.- This script controls the flow of the learning procedure and handle all the clerical tasks such as organizing, naming, and writing numerical results to disk. No direct modification to this file is needed to run the experiments in the above paper.
-
Execution scripts: all the files of the form
run_*.sh.- The choice of algorithm, model, data generation protocol, among other key parameters is made within these simple shell scripts. Basically, parameters are specified explicitly, and these are then passed to the driver script as options.
The experiments using real-world datasets require the user to run the driver scripts themselves; all the other experiments are self-contained within Jupyter notebooks.
This example uses run.sh and run_common.sh to execute tests with pre-fixed settings for multiple risk classes and multiple datasets.
(bdd-mv) bash run.sh adult australian cifar10 fashion_mnist
Of course, the above examples assume the user has (via mml or some other route) already obtained the datasets (adult, australian, cifar10, fashion_mnist) being specified, and that setup_data.py has been modified such that the program knows where to find the data.
This repository includes detailed demonstrations to walk the user through re-creating the results in the paper cited at the top of this document. Below is a list of demo links which give our demos (constructed in Jupyter notebook form) rendered using the useful nbviewer service.
- Examining the Sun-Huber dispersion function (sections 2 and 3 in paper)
- 2D classification tests using simulated data (section 4.1 in paper)
- Tests using real data benchmarks (section 4.2 in paper)
- Replace
[safe hash mml]with30b0f2be3f4b755c4ac9b1170883d983dc93a5fd.
Date of safe hash test: 2023/01/27.